El uso escolar del patrimonio cultural en las ciencias sociales y la contribución de lo digital: algunas líneas de investigación en Quebec
Bibliographic record
Abstract
The purpose of this article is to offer a perspective on research carried out in Québec since the last seven years on the school use of cultural heritage and digital media or resources in collaboration, for the most part, with cultural organizations, including galleries, libraries, archives and museums from different disciplinary fields. Works done by the author of this article and some of her colleagues are presented. The author's interest in educational intervention in the field of social sciences, and sometimes in the arts, has materialized through the collaborative development of cultural mediation devices that often involve digital platforms. The willingness of a growing number of heritage and cultural institutions to consider such integration to their cultural mediation practices (Goldman, 2011; SMQ, 2011) prompted the author to undertake some research-development (or "research and investigation") (Van Der Maren, 1996) of a collaborative nature. Thus, teachers and museum or cultural actors are part of the collaborators, in the context of association with institutions such as the Museum of Fine Arts of Montréal, the McCord Museum, the Boréalis Museum, the Bibliothèque et Archives nationales du Québec, and Télé-Québec. The general objective remains to establish links between culture and education, and thus contribute to update the essentially cultural mission of the school, using the resources of cultural organizations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".